Adaptive weighted convolutional neural network underwater sonar image classification method based on deep learning

Abstract

The present invention provides an adaptive weighted convolutional neural network underwater sonar image classification method based on deep learning. (1) Generate a two-dimensional parameter matrix of the deep belief network DBN according to the characteristics of the underwater sonar images in the data set; (2) Adaptively adjust the distribution of the weight matrix of the convolutional neural network CNN filter; (3) Implement deep learning-based Adaptive weighted convolutional neural network for underwater sonar image classification. The adaptive weighted convolutional neural network underwater sonar image classification method proposed by this invention based on deep learning can solve the random problem of filter weight initialization in CNN, avoid falling into local optimality, and can better improve classification accuracy. rate and has certain effectiveness.

Description

technical field [0001] The invention relates to a method for classifying underwater targets, in particular to a method for classifying underwater sonar images. Background technique [0002] In recent years, the underwater target classification technology based on sonar images has been greatly developed, and its application range is becoming wider and wider. Due to the low contrast, blurred edges, and weak texture of underwater sonar images, the image quality is not ideal, which will seriously affect the classification of underwater sonar images, making it a difficult problem. Scholars at home and abroad have conducted in-depth research on the classification of underwater sonar images and achieved important results. Among them, the most famous and effective classification methods in the existing literature mainly include: 1. Underwater sonar image classification based on image segmentation and texture features: 2012 Khidkikar Mahesh, Balasubramanian Ramprasad.Segmentation an...

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Problems solved by technology

[0003] The traditional underwater sonar image classification method uses different feature extraction methods to complete the classification, but a specific feature extraction method cannot take into account all the useful information of underwater sonar images, resulting in the bottleneck of underwater sonar image classification

In addition, due to the imaging of underwater sonar images, it is difficult to separate the targets of underwater sonar images from the shadow parts and the reverberation area of ​​the seabed. In addition, the seabed is complicated and noisy, which makes the classification of seabed targets difficult. become difficult

At present, the work on the classification of underwater sonar images is still in its infancy. The seabed is complicated, and the images captured by sonar on the seabed are different every time. Even one class of images is from various angles, which further increases The Difficulty of Classifying Underwater Sonar Images

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